## Step 1: Define the symbolic representation of the problem
Let's denote the fraction of cheap paint as $x$ and the fraction of expensive paint as $y$. The objective is to minimize the cost, which is $0.30x + 1.50y$. The quality constraint is $50x + 90y \geq 80$. Also, since $x$ and $y$ represent fractions of a liter, we have $x + y = 1$, and $x, y \geq 0$.

## 2: Convert the problem into a symbolic representation
The symbolic variables are:
- $x_1 = x$, representing the fraction of cheap paint
- $x_2 = y$, representing the fraction of expensive paint

The objective function to minimize is $0.30x_1 + 1.50x_2$.

The constraints are:
- $50x_1 + 90x_2 \geq 80$ (quality constraint)
- $x_1 + x_2 = 1$ (fraction constraint)
- $x_1, x_2 \geq 0$ (non-negativity constraint)

## 3: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'fraction of cheap paint'), ('x2', 'fraction of expensive paint')],
    'objective_function': '0.30*x1 + 1.50*x2',
    'constraints': ['50*x1 + 90*x2 >= 80', 'x1 + x2 = 1', 'x1 >= 0', 'x2 >= 0']
}
```

## 4: Implement the problem in Gurobi code
```python
import gurobi

def solve_paint_blend_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="cheap_paint", lb=0, ub=1)
    x2 = model.addVar(name="expensive_paint", lb=0, ub=1)

    # Set the objective function
    model.setObjective(0.30 * x1 + 1.50 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(50 * x1 + 90 * x2 >= 80, name="quality_constraint")
    model.addConstr(x1 + x2 == 1, name="fraction_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Fraction of cheap paint: {x1.x}")
        print(f"Fraction of expensive paint: {x2.x}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_paint_blend_problem()
```